Stand-alone Transcriptional Immune Response Prediction in Primary Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) accounts for 10% to 20% of primary breast cancers and often has early relapses and aggressive progression. An activated tumor immune response can be prognostic in patients with treatment-naïve and chemotherapy-treated TNBC and may be assessed using gene expression data. We derived a stand-alone predictor for a proposed immunomodulatory transcriptional TNBC subtype in a training cohort of 235 patients with primary disease based on random forest modeling of RNA sequencing data. Validation in independent TNBC cohorts totaling more than 1,200 patients demonstrated that the classifier recapitulates the immunomodulatory mRNA subtype classification, is associated with elevated immune expression and diversity of T-cell receptor genes, is associated with response to neoadjuvant chemotherapy, and can separate patients into subgroups with better or worse prognosis after adjuvant chemotherapy. The availability of stand-alone classifiers for mRNA-based prediction may further enhance RNA sequencing's usability in a more routine clinical context and for translational endpoints in clinical trials.

Tumor immune response has prognostic and treatment predictive value in TNBC and can be estimated by, e.g., mRNA profiling. Translating this association into classifications for single patients requires stand-alone predictors. We have developed one such mRNA classifier that could be applied in future clinical contexts and clinical trials.